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Institution

University of Siena

EducationSiena, Italy
About: University of Siena is a education organization based out in Siena, Italy. It is known for research contribution in the topics: Population & Cancer. The organization has 12179 authors who have published 33334 publications receiving 1008287 citations. The organization is also known as: Università degli studi di Siena & Universita degli studi di Siena.


Papers
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Journal ArticleDOI
TL;DR: Evidence is provided that at least 5 distinct RSN patterns are reproducible across different subjects and that RSNs are a major source of non-modeled signal in BOLD fMRI data, so a full understanding of their dynamics will improve the interpretation of functional brain imaging studies more generally.

1,236 citations

Journal ArticleDOI
TL;DR: As adjuvant therapy for high‐risk stage III melanoma, 200 mg of pembrolizumab administered every 3 weeks for up to 1 year resulted in significantly longer recurrence‐free survival than placebo, with no new toxic effects identified.
Abstract: Background The programmed death 1 (PD-1) inhibitor pembrolizumab has been found to prolong progression-free and overall survival among patients with advanced melanoma. We conducted a phase 3 double-blind trial to evaluate pembrolizumab as adjuvant therapy in patients with resected, high-risk stage III melanoma. Methods Patients with completely resected stage III melanoma were randomly assigned (with stratification according to cancer stage and geographic region) to receive 200 mg of pembrolizumab (514 patients) or placebo (505 patients) intravenously every 3 weeks for a total of 18 doses (approximately 1 year) or until disease recurrence or unacceptable toxic effects occurred. Recurrence-free survival in the overall intention-to-treat population and in the subgroup of patients with cancer that was positive for the PD-1 ligand (PD-L1) were the primary end points. Safety was also evaluated. Results At a median follow-up of 15 months, pembrolizumab was associated with significantly longer recurrence...

1,225 citations

Journal ArticleDOI
B. P. Abbott1, R. Abbott1, T. D. Abbott2, Sheelu Abraham3  +1271 moreInstitutions (145)
TL;DR: In 2019, the LIGO Livingston detector observed a compact binary coalescence with signal-to-noise ratio 12.9 and the Virgo detector was also taking data that did not contribute to detection due to a low SINR but were used for subsequent parameter estimation as discussed by the authors.
Abstract: On 2019 April 25, the LIGO Livingston detector observed a compact binary coalescence with signal-to-noise ratio 12.9. The Virgo detector was also taking data that did not contribute to detection due to a low signal-to-noise ratio, but were used for subsequent parameter estimation. The 90% credible intervals for the component masses range from to if we restrict the dimensionless component spin magnitudes to be smaller than 0.05). These mass parameters are consistent with the individual binary components being neutron stars. However, both the source-frame chirp mass and the total mass of this system are significantly larger than those of any other known binary neutron star (BNS) system. The possibility that one or both binary components of the system are black holes cannot be ruled out from gravitational-wave data. We discuss possible origins of the system based on its inconsistency with the known Galactic BNS population. Under the assumption that the signal was produced by a BNS coalescence, the local rate of neutron star mergers is updated to 250-2810.

1,189 citations

Journal ArticleDOI
B. P. Abbott1, Richard J. Abbott1, T. D. Abbott2, Matthew Abernathy3  +978 moreInstitutions (112)
TL;DR: The first observational run of the Advanced LIGO detectors, from September 12, 2015 to January 19, 2016, saw the first detections of gravitational waves from binary black hole mergers as discussed by the authors.
Abstract: The first observational run of the Advanced LIGO detectors, from September 12, 2015 to January 19, 2016, saw the first detections of gravitational waves from binary black hole mergers. In this paper we present full results from a search for binary black hole merger signals with total masses up to 100M⊙ and detailed implications from our observations of these systems. Our search, based on general-relativistic models of gravitational wave signals from binary black hole systems, unambiguously identified two signals, GW150914 and GW151226, with a significance of greater than 5σ over the observing period. It also identified a third possible signal, LVT151012, with substantially lower significance, which has a 87% probability of being of astrophysical origin. We provide detailed estimates of the parameters of the observed systems. Both GW150914 and GW151226 provide an unprecedented opportunity to study the two-body motion of a compact-object binary in the large velocity, highly nonlinear regime. We do not observe any deviations from general relativity, and place improved empirical bounds on several high-order post-Newtonian coefficients. From our observations we infer stellar-mass binary black hole merger rates lying in the range 9−240Gpc−3yr−1. These observations are beginning to inform astrophysical predictions of binary black hole formation rates, and indicate that future observing runs of the Advanced detector network will yield many more gravitational wave detections.

1,172 citations

Journal ArticleDOI
TL;DR: It is suggested that sorafenib is a new treatment option for patients with progressive radioactive iodine-refractory differentiated thyroid cancer and progression-free survival significantly improved in all prespecified clinical and genetic biomarker subgroups, irrespective of mutation status.

1,171 citations


Authors

Showing all 12352 results

NameH-indexPapersCitations
Johan Auwerx15865395779
I. V. Gorelov1391916103133
Roberto Tenchini133139094541
Francesco Fabozzi133156193364
M. Davier1321449107642
Roberto Dell'Orso132141292792
Rino Rappuoli13281664660
Teimuraz Lomtadze12989380314
Manas Maity129130987465
Dezso Horvath128128388111
Paolo Azzurri126105881651
Vincenzo Di Marzo12665960240
Igor Katkov12597271845
Ying Lu12370862645
Thomas Schwarz12370154560
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202391
2022221
20211,870
20201,979
20191,639
20181,523